> ML_LITERATURE // ZAHARIA-2018-ACCELERATING-MACHINE-LEARNING-LIFECYCLE-WITH-MLFLOW_v1.0
Accelerating the Machine Learning Lifecycle with MLflow
Matei Zaharia, Andrew Chen, Aaron Davidson, Ali Ghodsi, Sue Ann Hong, Andy Konwinski, Siddharth Murching, Tomas Nykodym, Paul Ogilvie, Mani Parkhe, Fen Xie, Clemens Mewald · IEEE Data Engineering Bulletin (2018)
mlops-production2018industry-standardnotAssessed
Principal Contribution
Designed an open-source platform standardizing experiment tracking, project reproducibility packaging, and centralized model registries across arbitrary ML libraries.
Operational Relevance
Serves as qualified reference for implementing task-experiment-tracking, task-model-registry in production systems.
Assumptions
- Underlying computational topology and mathematical bounds adhere to established convexity/smoothness guarantees
Limitations
- Hardware runtime speedups, privacy budgets, and convergence depend on hyperparameters and network communication limits
Connected Algorithms, Architectures & Tools
Related Algorithms:
Related Architectures:
Implementing Libraries: